SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network.

ABSTRACT: Recent advances in deep learning have significantly improved the ability to infer protein sequences directly from protein structures for the fix-backbone design. The methods have evolved from the early use of multi-layer perceptrons to convolutional neural networks, transformers, and graph neural networks (GNN). However, the conventional approach of constructing K-nearest-neighbors (KNN) graph for GNN has limited the utilization of edge information, which plays a critical role in network performance. Here we introduced SPIN-CGNN based on protein contact maps for nearest neighbors. Together with auxiliary edge updates and selective kernels, we found that SPIN-CGNN provided a comparable performance in refolding ability by AlphaFold2 to the current state-of-the-art techniques but a significan

SUBMITTER: Zhang X 

PROVIDER: S-EPMC10729952 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature